A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Douglas Alem*, Eduardo Curcio, Pedro Amorim, Bernardo Almada-Lobo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

Original languageEnglish
Pages (from-to)125-141
Number of pages17
JournalComputers and Operations Research
Volume90
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • empirical study
  • GLSP
  • lot sizing and scheduling problems
  • Monte Carlo simulation
  • robust optimization
  • stochastic programming

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